Purpose
– Globally expanding supply chains (SCs) have grown in complexity increasing the nature and magnitude of risks companies are exposed to. Effective methods to identify, model and analyze these risks are needed. Risk events often influence each other and rarely act independently. The SC risk management practices currently used are mostly qualitative in nature and are unable to fully capture this interdependent influence of risks. The purpose of this paper is to present a methodology and tool developed for multi-tier SC risk modeling and analysis.
Design/methodology/approach
– SC risk taxonomy is developed to identify and document all potential risks in SCs and a risk network map that captures the interdependencies between risks is presented. A Bayesian Theory-based approach, that is capable of analyzing the conditional relationships between events, is used to develop the methodology to assess the influence of risks on SC performance
Findings
– Application of the methodology to an industry case study for validation reveals the usefulness of the Bayesian Theory-based approach and the tool developed. Back propagation to identify root causes and sensitivity of risk events in multi-tier SCs is discussed.
Practical implications
– SC risk management has grown in significance over the past decade. However, the methods used to model and analyze these risks by practitioners is still limited to basic qualitative approaches that cannot account for the interdependent effect of risk events. The method presented in this paper and the tool developed demonstrates the potential of using Bayesian Belief Networks to comprehensively model and study the effects or SC risks. The taxonomy presented will also be very useful for managers as a reference guide to begin risk identification.
Originality/value
– The taxonomy developed presents a comprehensive compilation of SC risks at organizational, industry, and external levels. A generic, customizable software tool developed to apply the Bayesian approach permits capturing risks and the influence of their interdependence to quantitatively model and analyze SC risks, which is lacking.
As recent research continues to explore possibilities for developing renewable biofuels, there arises a need for a supporting framework that can guide the research along a path that will lead to long-term technoeconomic viability. Processing of biomass into various marketable products requires a well-planned strategy from an investment, agriculture, management, and policy-making perspective. This novel techno-economic analysis tool encompasses multiple process and supply chain models into a comprehensive decision support tool. Incorporation of detailed upstream and downstream processes not only gives an opportunity to accommodate fundamental research, but also allows for the incorporation of the effects of future uncertainties. This claim is further validated by performing a case study on a biological process to convert locally available corn stover to ethanol. The results obtained show that unique integration of process simulation, supply chain optimization, and discrete event simulation can be used to validate the long-term economic viability of a biorefining process. Analysis demonstrates that the developed decision support tool can be applied to estimate long-term economic and environmental viability of potential biorefining processes in any given region of interest.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.